Intrusion Modelling and the Effect of Ground Water Conditions
Bibliographic record
Abstract
Intrusion of contaminants into water distribution systems due to negative pressures is a complex phenomena that has been theorised and is an active area of research. Work has identified the existence of contaminants in soil and ground water surrounding pipes, and has investigated modelling the risk to human health should contaminants enter water distribution systems. However, there is a lack of understanding regarding the critical interaction between the pipe, the leak aperture and the surrounding ground and water. Typical intrusion models assume a simple orifice relationship, with inflow volumes proportional to the square root of the difference between the pipe pressure and the external hydrostatic pressure. This is shown here through computational modelling to be an overly simple relationship for leak behaviour that does not take into account the existence or properties of a porous media external to the pipe. In this paper the authors will discuss the construction of computational fluid dynamics (CFD) models of the intrusion process due to transient events and describe results that suggest the simple orifice equation is not a sufficient model. In the CFD calculations the surrounding ground water is modelled as a saturated porous media and it is shown that the properties of the media and the leak geometry have a large effect on the relationship between the pressures and flow rate. It is also shown that the risk of intrusion should be considered from contaminants that originate from both above and below the depth of the pipe. Further, the idea of a zone of influence surrounding the leak point, due to the leak size and the magnitude and duration of the negative pressure, is introduced as a possible measure of the level of risk of the intrusion event.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".